Step 1 to Transform Quality Management: Identify Dysfunctional Data
Dysfunctional data holds back manufacturing operations in many preventable ways.
In today’s high-stakes world of manufacturing, you can’t sit quietly by while your competitors implement continuous improvements. You can’t remain content with your organization’s status quo, or you risk being left behind. But to make meaningful improvements, you must first understand the underlying causes of performance and quality issues. And very often, those causes can be traced back to a single problem—data.
“Dysfunctional” is not a term that we typically use to describe quality management challenges or the operational data that we use to manage quality or manufacturing performance. But perhaps we should.
Data collection is the engine that drives your quality improvement efforts, whether they are continuous improvement, Six Sigma, Lean, statistical process control (SPC), or anything else. From the data you collect, you can discover things about your operations that you might never know from walking the plant floor.
But what happens when you can’t rely on your data? Dysfunctional data is any or all of the following:
If your data suffers from any of these things, then it’s time to take a cold, hard, serious look at how you collect it and what you do with it. To begin our journey from dysfunctional data to actionable intelligence, let’s walk through each of these data challenges one by one.
Incomplete data is simply missing critical details or missing altogether. If you don’t have access to, or are unable to leverage value from, any source of insight, you have incomplete data. Likewise, if you can’t fully describe your ongoing operational and quality processes, your data is incomplete.
The gap may be caused by unreliable or outdated data collection methods. For example, if your operators complete manual checks and then record that data on paper, you are likely to encounter incomplete data on a regular basis.
If quality and compliance checks are not performed when and how they should be, you’ll see gaps. Worse still, this data almost always remains on paper and is not analyzed to generate operational insights.
Additionally, you might not be capturing data that has intrinsic operational value. Capturing information about process parameters, operational events, or quality characteristics will provide greater insight into your manufacturing and quality processes.
When data is incomplete, the big picture of your manufacturing operation is missing some important pieces. You may have potentially valuable quality or operational information but be unable to leverage it for operational improvement.
Inconsistent data is collected, or available for collection, but is not standardized across manufacturing operations. Inconsistency can show up as variations in naming conventions, measurement units, sample rates, procedures and methods, or calculated metrics. Data might also be inconsistent across different processes, shifts, and plants.
Inconsistency makes cross-comparison of your data nearly impossible. If you can’t compare “apples to apples” across the various areas of your manufacturing operations, you will never get a big-picture understanding of how everything is really performing.
Overcoming inconsistency is not hard—if you’re using systems that support standardization of processes and data collection and provide a means of aggregating that data and making it easily consumable and understandable by managers, engineers, and quality professionals. (A full-featured, cloud-based quality management solution can provide the centralized control needed rel="nofollow".)
When disparate data is held in different systems and in different formats—such as paper records, spreadsheets, Enterprise Resource Planning/Manufacturing Execution System (ERP/MES), or proprietary legacy systems—that data is isolated.
Isolated data may be inaccessible, or siloed—that is, located in a remote location, perhaps in separate systems, or in a proprietary format. Or your data may exist only on paper forms, which means that it's only available if you're physically looking at that piece of paper.
No matter how your data is isolated, it’s on its own and cannot be easily compared, contrasted, or analyzed (like it should be). It can’t give up the “golden nuggets” of insight it inevitably contains—insights you can use to improve your processes.
Although isolated data may serve its primary purpose (e.g., verifying a required check), its value can rarely be extended beyond that. (To learn more about the Second Life of Data, check out this on-demand webinar.)
Data collection and reporting that comprise a manual process can be resource-intensive and time-consuming—thus, inefficient. It’s surprising to discover how many modern companies are still collecting, analyzing, and reporting on their data with paper and pencil, or unwieldy spreadsheets.
Manual data collection can be slow and fraught with errors. Paper charts can be cumbersome and difficult to analyze, collate, cross-reference, and share.
Likewise, reporting using arcane methods is a time killer. When data is presented in densely populated tabular reports or spreadsheets, interpreting and gaining meaningful insight from it is difficult. Too many people spend too many hours collecting, analyzing, and presenting information that, quite frankly, a solid quality management software system can deliver instantly.
The Impact of Dysfunctional Data
Dysfunctional data inevitably leads to three fundamental operational challenges: impaired operational visibility, uninformed decision making, and quality compliance risks.
Impaired operational visibility
Because of dysfunctional data, you don’t have the 360-degree view of your manufacturing and quality operations that you need to institute real change and improvement.
When you lack insight, you’re either not making the decisions you should be making or you’re making them without reliable facts or evidence to support them. You may even be unaware of the important decisions that need to be made.
Quality compliance risks
When impaired operational visibility leads to uninformed decision-making, you are exposed to unnecessary risks. These risks may impact performance daily on an operational level. Or you may be exposed to higher-level strategic risks such as a failure to comply with internal or external quality standards or customer-, government-, or industry-specific requirements.
Dysfunctional data not only exposes manufacturers to greater risk but also impedes their ability to improve operational performance. The good news is that you can meet the challenge of dysfunctional data head-on. When you leverage modern technology and techniques, you can move away from dysfunctional data and towards what we call actionable intelligence.
As manufacturers deal with dynamic and dramatic shifts in their businesses, many are taking the opportunity to consider re-thinking their approach to managing quality and operational data. Learn how you can take advantage of the latest Quality Intelligence technology now by visiting the InfinityQS website.